Application of Clustering Algorithm in Audit Stratified Sampling

DOI

10.2991/wartia-18.2018.74How to use a DOI?

Keywords

Audit Stratified Sampling,clustering algorithm, audit stratified samplintherapeutic drugs, selection strategy, fusion function

Abstract

Clustering fusion is a large combination of different algorithms or the same algorithm using different parameters the members of quantitative clustering are fused by fusion function, and the final clustering results are obtained. Clustering fusion has become a research hotspot in the field of data mining. However, the traditional clustering fusion method the method usually involves all the cluster members produced. But in supervised classification learning, Great progress has been made in the selection of classification fusion, and the selectivity for unsupervised classification has been improved. Clustering fusion has been paid more and more attention only in recent years. The study shows that the selective clustering fusion the combined method can improve the accuracy of clustering analysis. This paper aims at selective polymerization. Data dimensionality reduction, selection strategy, fusion function design and other algorithms in class fusion are studied. The selective clustering fusion algorithm is applied to the analysis of multiple clustering problems.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yawen Xiao
AU  - Pengwu Wang
PY  - 2018/09
DA  - 2018/09
TI  - Application of Clustering Algorithm in Audit Stratified Sampling
BT  - Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)
PB  - Atlantis Press
SP  - 401
EP  - 406
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-18.2018.74
DO  - 10.2991/wartia-18.2018.74
ID  - Xiao2018/09
ER  -